available technology (Khan & Adams, 2016), and the
research has found serious obstacles to fully integrate technology into educational processes (Cuban, Kirkpatrick, &
C.P, 2001).
An exploratory study was conducted at King Fahd
University of Petroleum and Minerals (KFUPM), Saudi
Arabia to identify if there is a lack of LMS adoption in the
institution. After the exploratory study, it was discovered
that LMS technology has been made available by the academic institution but it is not being used to its full potential.
A similar problem was identified by Cheng, Wang, Moormann, Olaniran, and Chen (2012), Dutton, Cheong, and Park
(2004), and Khan and Adams (2016). The built-in functionalities and features of LMS systems to improve teaching
and learning services are also underutilized (Sharma et al.,
2011). Therefore, it is essential to investigate the determinants that influence the instructors’ behavioral intentions to
use the LMS at SHEIs.
This book addresses the gaps that have been identified
through the literature review on the adoption of technology,
variables related to technology adoption, models of technology acceptance, and cultural theories. Most technology
acceptance models were established and tested in Western
cultures (Al-Gahtani et al., 2007). However, a few published
studies explored the adequacy of the models in non-Western
cultures, especially Saudi Arabia. It would be naïve to
assume that such a technology adoption model can be
equally applicable in all cultural settings, especially in
developing nations such as Saudi Arabia (Al-Gahtani et al.,
2007). It is a well-recognized fact that cultural characteristics
play a key role in technology adoption, yet cultural variables
are ignored in most technology adoption models (Lin, 2014).
Many researchers argue that cultural variables need to be
incorporated in technology adoption models (Baptista &
Oliveira, 2015; Lu & Lin, 2012) because information technology used by the people is impacted by cultural values
(Im, Hong, & Kang, 2011). The original UTAUT2 model by
Venkatesh et al. (2012) does not talk about cultural variables
and lacks cultural awareness in the non-Western countries.
Furthermore, the original UTAUT2 model was established
and validated in the context of mobile phone consumer
research. The literature shows that it has not been extensively adopted in educational settings to test the acceptance
and use of LMS. Therefore, it is very important to achieve a
better knowledge of the impact of culture and other variables
on the adoption of LMS. Thus, the purpose of this research
is to employ the extended version of ‘unified theory of
acceptance and use of technology’ (i.e., UTAUT2) as a
framework for determining behavioral intention linked with
the adoption and use of an LMS among instructors at HEIs.
The study in this book also attempts to determine the validity
of the UTAUT2 model in non-Western cultures. Thus, the
study in this book extends the UTAUT2 model with
Hofstede’s (1980) cultural dimensions and technology
awareness (TA) as moderators of the model. With the
extension of the UTAUT2 model, it forms a new theoretical
model that could help understand user behavior associated
with the adoption of LMS in the cultural context of higher
educational institutions (HEIs). One of the prime incentives
for this research is to explore the viability of the UTAUT2
model in non-Western countries, such as Saudi Arabia, and
to suggest some of the ways the institutions can improve the
adoption of LMS among instructors of these higher educational institutions.
1.1.2 Research Objective and Research
Questions (RQs)
Before defining RQs, it is imperative to understand the
definitions of independent and dependent variables used in
this research.
• Performance Expectancy (PE): Venkatesh et al.
(2003) defined performance expectancy as “the degree to
which an individual believes that using the system will
help him or her to attain gains in job performance”
(p. 447).
• Effort Expectancy (EE): Venkatesh et al. (2003) defined
that effort expectancy is “the degree of ease associated
with the use of the system” (p. 450).
• Social Influence (SI): Social influence (SI) includes the
social pressure exercised on a person by the beliefs of
other individuals or groups. The social influence is “the
degree to which an individual perceives that important
others believe he or she should use the new system”
(p. 451).
• Facilitating Conditions (FC): Venkatesh et al.
(2003) defined that facilitating conditions are “the degree
to which an individual believes that an organizational and
technical infrastructure exists to support the use of the
system” (p. 453).
• Hedonic Motivation (HM): Venkatesh et al. (2012) defined hedonic motivation as “the fun or pleasure derived
from using a technology” (p. 161).
• Habit (H) is the automatic behavior that enables learning
on how to use the technology. In other words, habit is the
automaticity of behavior associated with the use of
technology over time. Venkatesh et al. (2012) cited
Limayem, Hirt, and Cheung (2007) that “habit is the
extent to which people tend to perform behaviors automatically because of learning” (p. 161).
• Use Behavior (UB) is the actual use of the technology
(Venkatesh et al. 2012).
• Behavioral Intention (BI): According to the theory of
reasoned action by Fishbein and Ajzen (1975) and theory
2
1 Adoption of LMS in the Cultural Context of Higher Educational …
research has found serious obstacles to fully integrate technology into educational processes (Cuban, Kirkpatrick, &
C.P, 2001).
An exploratory study was conducted at King Fahd
University of Petroleum and Minerals (KFUPM), Saudi
Arabia to identify if there is a lack of LMS adoption in the
institution. After the exploratory study, it was discovered
that LMS technology has been made available by the academic institution but it is not being used to its full potential.
A similar problem was identified by Cheng, Wang, Moormann, Olaniran, and Chen (2012), Dutton, Cheong, and Park
(2004), and Khan and Adams (2016). The built-in functionalities and features of LMS systems to improve teaching
and learning services are also underutilized (Sharma et al.,
2011). Therefore, it is essential to investigate the determinants that influence the instructors’ behavioral intentions to
use the LMS at SHEIs.
This book addresses the gaps that have been identified
through the literature review on the adoption of technology,
variables related to technology adoption, models of technology acceptance, and cultural theories. Most technology
acceptance models were established and tested in Western
cultures (Al-Gahtani et al., 2007). However, a few published
studies explored the adequacy of the models in non-Western
cultures, especially Saudi Arabia. It would be naïve to
assume that such a technology adoption model can be
equally applicable in all cultural settings, especially in
developing nations such as Saudi Arabia (Al-Gahtani et al.,
2007). It is a well-recognized fact that cultural characteristics
play a key role in technology adoption, yet cultural variables
are ignored in most technology adoption models (Lin, 2014).
Many researchers argue that cultural variables need to be
incorporated in technology adoption models (Baptista &
Oliveira, 2015; Lu & Lin, 2012) because information technology used by the people is impacted by cultural values
(Im, Hong, & Kang, 2011). The original UTAUT2 model by
Venkatesh et al. (2012) does not talk about cultural variables
and lacks cultural awareness in the non-Western countries.
Furthermore, the original UTAUT2 model was established
and validated in the context of mobile phone consumer
research. The literature shows that it has not been extensively adopted in educational settings to test the acceptance
and use of LMS. Therefore, it is very important to achieve a
better knowledge of the impact of culture and other variables
on the adoption of LMS. Thus, the purpose of this research
is to employ the extended version of ‘unified theory of
acceptance and use of technology’ (i.e., UTAUT2) as a
framework for determining behavioral intention linked with
the adoption and use of an LMS among instructors at HEIs.
The study in this book also attempts to determine the validity
of the UTAUT2 model in non-Western cultures. Thus, the
study in this book extends the UTAUT2 model with
Hofstede’s (1980) cultural dimensions and technology
awareness (TA) as moderators of the model. With the
extension of the UTAUT2 model, it forms a new theoretical
model that could help understand user behavior associated
with the adoption of LMS in the cultural context of higher
educational institutions (HEIs). One of the prime incentives
for this research is to explore the viability of the UTAUT2
model in non-Western countries, such as Saudi Arabia, and
to suggest some of the ways the institutions can improve the
adoption of LMS among instructors of these higher educational institutions.
1.1.2 Research Objective and Research
Questions (RQs)
Before defining RQs, it is imperative to understand the
definitions of independent and dependent variables used in
this research.
• Performance Expectancy (PE): Venkatesh et al.
(2003) defined performance expectancy as “the degree to
which an individual believes that using the system will
help him or her to attain gains in job performance”
(p. 447).
• Effort Expectancy (EE): Venkatesh et al. (2003) defined
that effort expectancy is “the degree of ease associated
with the use of the system” (p. 450).
• Social Influence (SI): Social influence (SI) includes the
social pressure exercised on a person by the beliefs of
other individuals or groups. The social influence is “the
degree to which an individual perceives that important
others believe he or she should use the new system”
(p. 451).
• Facilitating Conditions (FC): Venkatesh et al.
(2003) defined that facilitating conditions are “the degree
to which an individual believes that an organizational and
technical infrastructure exists to support the use of the
system” (p. 453).
• Hedonic Motivation (HM): Venkatesh et al. (2012) defined hedonic motivation as “the fun or pleasure derived
from using a technology” (p. 161).
• Habit (H) is the automatic behavior that enables learning
on how to use the technology. In other words, habit is the
automaticity of behavior associated with the use of
technology over time. Venkatesh et al. (2012) cited
Limayem, Hirt, and Cheung (2007) that “habit is the
extent to which people tend to perform behaviors automatically because of learning” (p. 161).
• Use Behavior (UB) is the actual use of the technology
(Venkatesh et al. 2012).
• Behavioral Intention (BI): According to the theory of
reasoned action by Fishbein and Ajzen (1975) and theory
2
1 Adoption of LMS in the Cultural Context of Higher Educational …